Brain Tumor Classification Using Deep Learning Methods


avatar Mohammad Abbasi 1 , * , avatar Behnaz Eslami 1 , avatar Zahra Rezaei 2

Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Computer and Electrical Engineering, University of Kashan, Kashan, Iran

how to cite: Abbasi M , Eslami B, Rezaei Z. Brain Tumor Classification Using Deep Learning Methods. Innov J Radiol. 2019;16(Special Issue):e99160.



A World Health Organization (WHO) February 2018 report recently has shown that the rate of deaths because of brain or central nervous system (CNS) cancer has the highest rate in the Asian continent. Timely and accurate diagnosis of brain tumor is crucial where small errors pose many risks to treatment. Classifying the types of tumors is an important factor in targeted treatment. Since tumor diagnosis is highly invasive, time-consuming, and costly, there is an urgent need for a precise tool to develop a non-invasive, cost-effective, and efficient tool for brain tumor description and grade estimation. Brain scans by using magnetic resonance imaging (MRI), computed tomography (CT), and other imaging techniques are fast and safe to detect tumors.


In this paper, we used a standard dataset containing 3064 images from different skull views. The size and position of tumors at different angles make it difficult to detect the tumor in the specimens. This MRI dataset consisted of 3064 slices and 1047 coronal images. Coronal images were recorded from behind. Axial images taken from above included 990 images. Also, there were 1027 sagittal images extracted from the skull side. Images in this dataset belonged to 233 patients. The dataset consisted of 708 Meningioma, 1426 Glioma, and 930 Pituitary tumors; thus, we isolated images from different angles of sagittal, coronal, and axial images and then trained them in different categories by using Inception-V3 and Resent, which are deep learning classification methods to make this process more accurate and faster.


Finally, by adjusting the hyper-parameters of each of these methods with performing pre- processing and weighting combinations, we obtained an acceptable evaluation compared to previous methods.
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